code: def _compute_binary_demographic_parity(
      tp: torch.Tensor,
      fp: torch.Tensor,
      tn: torch.Tensor,
      fn: torch.Tensor,
  ) -> Dict[str, torch.Tensor]:
      """Compute demographic parity based on the binary stats."""
      pos_rates = _safe_divide(tp + fp, tp + fp + tn + fn)
      min_pos_rate_id = torch.argmin(pos_rates)
      max_pos_rate_id = torch.argmax(pos_rates)
  
      return {
          f"DP_{min_pos_rate_id}_{max_pos_rate_id}": _safe_divide(
              pos_rates[min_pos_rate_id], pos_rates[max_pos_rate_id]
          )
      }
  
file_path: torchmetrics/src/torchmetrics/functional/classification/group_fairness.py
imports: from typing import Dict, List, Optional, Tuple
  import torch
  from typing_extensions import Literal
  from torchmetrics.functional.classification.stat_scores import _binary_stat_scores_arg_validation, _binary_stat_scores_format, _binary_stat_scores_tensor_validation, _binary_stat_scores_update
  from torchmetrics.utilities import rank_zero_warn
  from torchmetrics.utilities.compute import _safe_divide
  from torchmetrics.utilities.data import _flexible_bincount
label_desc: Use image node_id 7 for calling a global function with example usage: _compute_binary_demographic_parity(tp, fp, tn, fn) and returns: dict